• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.6
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• pp.475-479
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• 2021

There are many difficulties in the applications of smart manufacturing technology in the era of the 4th industrial revolution. In this paper, a test bed was built to aim for acquiring smart manufacturing technology, and the test bed was designed to acquire basic technologies necessary for PLC (Programmable Logic Controller), HMI, Internet of Things (IoT), artificial intelligence (AI) and big data. By building a vehicle maintenance lift that can be easily accessed by the general public, PLC control technology and HMI drawing technology can be acquired, and by using cloud services, workers can respond to emergencies and alarms regardless of time and space. In addition, by managing and monitoring data for smart manufacturing, it is possible to acquire basic technologies necessary for embedded systems, the Internet of Things, artificial intelligence, and big data. It is expected that the improvement of smart manufacturing technology capability according to the results of this study will contribute to the effect of creating added value according to the applications of smart manufacturing technology in the future.

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.81-90
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• 2019

The data rate for transmission through fiber-optic cables has increased to 400 Gbps in single-wavelength channels. However, speeds up to 1 Tbps are required now to meet the ever-increasing bandwidth demand driven by the diverse requirements of contemporary applications for high-quality on-demand video streaming, cloud services, various social media, and emerging 5G-enabled applications. Because the data rates of the per-channel optical interfaces depend strongly on the operational speed of the optoelectronic devices used in optical transceivers, ultrahigh-speed photonic devices and components, and eventually, chip-level transmitter and receiver technologies, are essentially required to realize futuristic optical transceivers with data rates of 1 Tbps and beyond. In this paper, we review the recent progress achieved in high-speed optoelectronic devices, such as laser diodes, optical modulators, photodiodes, and the transmitter-receiver optical subassembly for optical transceivers in data centers and in metro/long-haul transmission.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.615-619
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• 2021

This study focuses on memory systems that are optimized to increase performance and energy efficiency in many embedded systems such as IoT, cloud computing, and edge computing, and proposes a performance improvement technique. The proposed technique improves memory system performance based on machine learning algorithms that are widely used in many applications. The machine learning technique can be used for various applications through supervised learning, and can be applied to a data classification task used in improving memory system performance. Data classification based on highly accurate machine learning techniques enables data to be appropriately arranged according to data usage patterns, thereby improving overall system performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Annual Conference of KIPS
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• 2023.05a
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• pp.339-341
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• 2023

This paper presents a multithread and efficient cryptographic hardware access (MECHA) for efficient and fast cryptographic operations that eliminates the need for context switching. Utilizing a UNIX domain socket, MECHA manages multiple requests from multiple applications simultaneously, resulting in faster processing and improved efficiency. We comprise several key components, including the Server thread, Client thread, Transceiver thread, and a pair of Sender and Receiver queues. MECHA design is portable and can be used with any communication protocol, with experimental results demonstrating a 83% increase in the speed of concurrent cryptographic requests compared to conventional interface design. MECHA architecture has significant potential in the field of secure communication applications ranging from cloud computing to the IoT, offering a faster and more efficient solution for managing multiple cryptographic operation requests concurrently.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.252-256
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• 2014

Traditional block cipher Advanced Encryption Standard (AES) is widely used in the field of network security, but it has high overhead on each operation. In the 15th international workshop on information security applications, a novel lightweight and low-power encryption algorithm named low-power encryption algorithm (LEA) was released. This algorithm has certain useful features for hardware and software implementations, that is, simple addition, rotation, exclusive-or (ARX) operations, non-Substitute-BOX architecture, and 32-bit word size. In this study, we further improve the LEA encryptions for cloud computing. The Web-based implementations include JavaScript and assembly codes. Unlike normal implementation, JavaScript does not support unsigned integer and rotation operations; therefore, we present several techniques for resolving this issue. Furthermore, the proposed method yields a speed-optimized result and shows high performance enhancements. Each implementation is tested using various Web browsers, such as Google Chrome, Internet Explorer, and Mozilla Firefox, and on various devices including personal computers and mobile devices. These results extend the use of LEA encryption to any circumstance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.310-311
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• 2016

마리타임 클라우드는 이네비게이션 실증 사례로써 통신 프레임워크를 지원하는 오픈소스 프로젝트이다. 마리타임 클라우드는 해양이해관계자들 사이의 안전한 정보교환을 제공하며 해양 서비스에 대한 관리를 지원한다. 그러나 마리타임 클라우드를 처음 접하는 개발자들에게 시스템을 이해하고 사용하기까지 많은 시간과 선행 작업이 요구된다. 이러한 점을 고려해 본 논문에서는 마리타임 클라우드에 쉽게 접근할 수 있고 초기비용을 감소시킬 수 있는 개발 지원 클래스를 설계하고 구현한다. 개발 지원 클래스는 마리타임 클라우드 접속과정과 메시지 전달기능인 MMS 관련 함수를 제공한다. 개발 지원 클래스를 이용하여 메시지 교환 프로그램을 구현하고 메시지 전달이 성공적으로 이루어졌는지 테스트한다.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1665-1673
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• 2003

We propose an image-based three-dimensional shape determination system. The shape, and thus the three-dimensional coordinate information of the 3-D object, is determined solely from captured images of the 3-D object from a prescribed set of viewpoints. The approach is based on the shape-from-silhouette (SFS) technique, and the efficacy of the SFS method is tested using a sample data set. The extracted three-dimensional shape is modeled with polygons generated by a new iterative triangulation algorithm, and the polygon model can be exported to commercial software. The proposed system may be used to visualize the 3-D object efficiently, or to quickly generate initial CAD data for reverse engineering purposes, including three dimensional design applications such as 3-D animation and 3-D games.